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Microenvironment

Source: R/Microenvironment.R
Microenvironment.Rd

Models local microenvironment cues that influence antibody behavior based on the density and structure of nearby data points.

Details

In real immunity, B cell fate is strongly influenced by local signals: chemokines, cytokines, and interactions with stromal cells in specific tissue microenvironments. This module translates that concept into density-dependent adaptation:

  • High density zones: Promote memory formation (stabilize antibodies, reduce mutation rate)

  • Low density zones: Promote exploration (increase mutation rate for broader search)

  • Boundary zones: Trigger class switching (change matching breadth between IgM-like broad and IgG-like specific modes)

  • Chemokine-like gradients: Bias mutation direction toward higher-density regions

Public fields

density_bandwidth

Bandwidth for kernel density estimation.

high_density_threshold

Density percentile above which antibodies stabilize.

low_density_threshold

Density percentile below which antibodies explore.

stabilization_factor

Mutation rate multiplier for high-density zones.

exploration_factor

Mutation rate multiplier for low-density zones.

last_densities

Per-antibody local density from last assessment.

last_zones

Per-antibody zone classification from last assessment.

Methods

Public methods

Method new()

Create a new Microenvironment module.

Usage

Microenvironment$new(
  density_bandwidth = NULL,
  high_density_threshold = 0.75,
  low_density_threshold = 0.25,
  stabilization_factor = 0.3,
  exploration_factor = 2
)

Arguments

density_bandwidth

Numeric. KDE bandwidth (NULL for auto).

high_density_threshold

Numeric [0,1]. Percentile threshold for stabilization.

low_density_threshold

Numeric [0,1]. Percentile threshold for exploration.

stabilization_factor

Numeric. Mutation rate multiplier for stable zones.

exploration_factor

Numeric. Mutation rate multiplier for exploration zones.

Method assess()

Assess the microenvironment around each antibody.

Usage

Microenvironment$assess(
  repertoire,
  X,
  affinityFunc = "gaussian",
  affinityParams = list(alpha = 1, c = 1, p = 2)
)

Arguments

repertoire

An ImmuneRepertoire object.

X

Numeric matrix of training data (n x d).

affinityFunc

Character. Affinity function.

affinityParams

List. Affinity parameters.

Returns

Named list with densities, zones, and mutation_modifiers per antibody.

Method print()

Print summary.

Usage

Microenvironment$print(...)

Arguments

...

Not used.

Method clone()

The objects of this class are cloneable with this method.

Usage

Microenvironment$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Assess microenvironment around antibodies
data(iris)
X <- as.matrix(iris[, 1:4])
me <- Microenvironment$new()
rep <- ImmuneRepertoire$new(X[sample(150, 15), ])
env <- me$assess(rep, X)
table(env$zones)  # stable, explore, boundary
#> 
#> boundary  explore   stable 
#>        7        4        4 
env$mutation_modifiers  # per-antibody rate scaling
#>  [1] 1.0 2.0 0.3 1.0 1.0 1.0 0.3 1.0 2.0 0.3 1.0 0.3 1.0 2.0 2.0